Setup

knitr::opts_chunk$set(echo = TRUE)

library(tidyverse)
library(R.utils)
library(wbCorr)
library(readxl)
library(kableExtra)
library(brms)
library(bayesplot)
library(beepr)


source(file.path('Functions', 'ReportModels.R'))
source(file.path('Functions', 'PrettyTables.R'))
source(file.path('Functions', 'ReportMeasures.R'))
source(file.path('Functions', 'PrepareData.R'))
system("shutdown /a")
## [1] 1116
# Set options for analysis
use_mi = FALSE
shutdown = FALSE
report_ordinal = FALSE

options(
  dplyr.print_max = 100, 
  brms.backend = 'cmdstan',
  brms.file_refit = ifelse(use_mi, 'never', 'on_change'),
  error = function() beepr::beep(sound = 5)
)
df <- openxlsx::read.xlsx(file.path('long.xlsx'))
df_original <- df

df_double <- prepare_data(df, use_mi = use_mi)[[1]]

Constructing scales Re-coding pusing reshaping data (4field) centering data within and between

Modelling

# For indistinguishable Dyads
model_rows_fixed <- c(
    'Intercept', 
    # '-- WITHIN PERSON MAIN EFFECTS --', 
    'persuasion_self_cw', 
    'persuasion_partner_cw', 
    'pressure_self_cw', 
    'pressure_partner_cw', 
    'pushing_self_cw', 
    'pushing_partner_cw', 
    'day', 
    'weartime_self_cw',
    
    'support_self_cw',
    'support_partner_cw',
    'isWeekendWeekend',
    'got_JITAI_selfJITAIreceived',
    'skilled_supportDaysafterIntervention',
    
    
    # '-- BETWEEN PERSON MAIN EFFECTS',
    'persuasion_self_cb',
    'persuasion_partner_cb',
    'pressure_self_cb',
    'pressure_partner_cb',
    'pushing_self_cb',
    'pushing_partner_cb',
    'weartime_self_cb',
    'studyGroupFirst3weeksinterventions',
    'studyGrouplast3weeksinterventions'
  )


model_rows_fixed_ordinal <- c(
  model_rows_fixed[1],
  'Intercept[1]',
  'Intercept[2]',
  'Intercept[3]',
  'Intercept[4]',
  'Intercept[5]',
  model_rows_fixed[2:length(model_rows_fixed)]
)

model_rows_random <- c(
  # '--------------',
  # '-- RANDOM EFFECTS --',
  'sd(Intercept)', 
  'sd(persuasion_self_cw)',
  'sd(persuasion_partner_cw)',
  'sd(pressure_self_cw)',
  'sd(pressure_partner_cw)',
  'sd(pushing_self_cw)',
  'sd(pushing_partner_cw)',
  # '-- CORRELATION STRUCTURE -- ', 
  'ar[1]', 
  'nu',
  'shape',
  'sderr',
  'sigma'
)

model_rows_random_ordinal <- c(model_rows_random,'disc')
# For indistinguishable Dyads
model_rownames_fixed <- c(
    'Intercept', 
    # '-- WITHIN PERSON MAIN EFFECTS --', 
    'Daily perceived persuasion target -> target', 
    'Daily perceived persuasion target -> agent', 
    'Daily perceived pressure target -> target', 
    'Daily perceived pressure target -> agent', 
    'Daily perceived pushing target -> target', 
    'Daily perceived pushing target -> agent', 
    'Day', 
    'Daily weartime',
    
    'Daily perceived support target -> target',
    'Daily perceived support target -> agent',
    'Is a weekend',
    'JITAI received',
    'Days post skilled support intervention',
    
    # '-- BETWEEN PERSON MAIN EFFECTS',
    'Mean perceived persuasion target -> target',
    'Mean Perceived persuasion target -> agent',
    'Mean Perceived pressure target -> target',
    'Mean Perceived pressure target -> agent',
    'Mean Perceived pushing target -> target',
    'Mean Perceived pushing target -> agent',
    'Mean weartime',
    
    'Difference study group 2',
    'Difference study group 3'
  )


model_rownames_fixed_ordinal <- c(
  model_rownames_fixed[1],
  'Intercept[1]',
  'Intercept[2]',
  'Intercept[3]',
  'Intercept[4]',
  'Intercept[5]',
  model_rownames_fixed[2:length(model_rownames_fixed)]
)

model_rownames_random <- c(
  # '--------------',
  # '-- RANDOM EFFECTS --',
  'sd(Intercept)', 
  'sd(Daily perceived persuasion target -> target)', 
  'sd(Daily perceived persuasion target -> agent)', 
  'sd(Daily perceived pressure target -> target)', 
  'sd(Daily perceived pressure target -> agent)', 
  'sd(Daily perceived pushing target -> target)', 
  'sd(Daily perceived pushing target -> agent)', 
  # '-- CORRELATION STRUCTURE -- ', 
  'ar[1]', 
  'nu',
  'shape',
  'sderr',
  'sigma'
)

model_rownames_random_ordinal <- c(model_rownames_random,'disc')
rows_to_pack <- list(
  "Within-Person Effects" = c(2,14),
  "Between-Person Effects" = c(15,23),
  "Random Effects" = c(24, 30), 
  "Additional Parameters" = c(31,35)
  )

rows_to_pack_ordinal <- list(
  "Intercepts" = c(1,6),
  "Within-Person Effects" = c(2+5,14+5),
  "Between-Person Effects" = c(15+5,23+5),
  "Random Effects" = c(24+5, 30+5), 
  "Additional Parameters" = c(31+5,35+6)
  )

Subjective MVPA

range(df_double$pa_sub, na.rm = T) 
## [1]   0 720
hist(df_double$pa_sub, breaks = 100)

Modelling using the gaussian family fails. Due to the many zeros, transformations won’t help estimating the models. We employ the negative binomial family.

formula <- bf(
  pa_sub ~ 
    persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw +
    #barriers_self_cw +
    
    support_self_cw + support_partner_cw + 
    #comf_self_cw + reas_self_cw + 
    isWeekend + 
    got_JITAI_self + skilled_support +
    
    persuasion_self_cb + persuasion_partner_cb +
    pressure_self_cb + pressure_partner_cb +
    pushing_self_cb + pushing_partner_cb +
    
    
    studyGroup +
    
    day + 
    
    # Random effects
    (persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw | coupleID),

  autocor = ~ ar(time = day, gr = coupleID:userID, p = 1)
)




prior1 <- c(
  brms::set_prior("normal(0, 2.5)", class = "b"),
  brms::set_prior("normal(0, 50)", class = "Intercept", lb = 0),
  
  brms::set_prior("normal(0, 10)", class = "sd", group = "coupleID", lb = 0),
  
  brms::set_prior("cauchy(0, 5)", class = "ar", lb = -1, ub = 1),
  brms::set_prior("cauchy(0, 20)", class = "shape"), 
  brms::set_prior("cauchy(0, 10)", class='sderr')
)

#df_minimal <- df_double[, c("userID", all.vars(as.formula(formula)))]

pa_sub <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = brms::negbinomial(),
  #control = list(adapt_delta = 0.99),
  iter = 5000,
  warmup = 2000,
  chains = 4,
  cores = 4,
  seed = 7777,
  file = file.path("models_cache_brms", "all_covariates_pa_sub")
)
pp_check(pa_sub, type='hist')
## Using 10 posterior draws for ppc type 'hist' by default.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

pp_check(pa_sub)
## Using 10 posterior draws for ppc type 'dens_overlay' by default.

loo(pa_sub)
## 
## Computed from 12000 by 3732 log-likelihood matrix.
## 
##          Estimate    SE
## elpd_loo -12058.6 177.5
## p_loo        42.3   3.3
## looic     24117.2 355.0
## ------
## MCSE of elpd_loo is NA.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.3, 1.8]).
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. ESS
## (-Inf, 0.7]   (good)     3723  99.8%   543     
##    (0.7, 1]   (bad)         9   0.2%   <NA>    
##    (1, Inf)   (very bad)    0   0.0%   <NA>    
## See help('pareto-k-diagnostic') for details.
plot(pa_sub, ask = FALSE)

summarize_brms(
  pa_sub, 
  model_rows_fixed = model_rows_fixed,
  model_rows_random = model_rows_random,
  model_rownames_fixed = model_rownames_fixed,
  model_rownames_random = model_rownames_random,
  exponentiate = T) %>%
  print_df(rows_to_pack = rows_to_pack)
IRR l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 31.94* 20.58 50.03 1.001 1913.66 4895.73
Within-Person Effects
Daily perceived persuasion target -> target 1.03 0.91 1.16 1.000 11507.83 9256.28
Daily perceived persuasion target -> agent 0.99 0.88 1.12 1.000 11873.53 9022.10
Daily perceived pressure target -> target 1.22 0.92 1.66 1.000 13031.30 8793.75
Daily perceived pressure target -> agent 0.93 0.72 1.25 1.000 10896.75 8513.11
Daily perceived pushing target -> target 1.05 0.89 1.24 1.000 12935.56 8917.69
Daily perceived pushing target -> agent 0.95 0.80 1.14 1.000 11595.76 9453.42
Day 0.91 0.58 1.42 1.001 8994.22 9509.62
Daily weartime NA NA NA NA NA NA
Daily perceived support target -> target 0.91* 0.84 0.99 1.000 12435.39 9090.73
Daily perceived support target -> agent 0.98 0.90 1.07 1.000 12356.19 8894.80
Is a weekend 1.03 0.84 1.27 1.001 14664.51 8949.57
JITAI received 1.02 0.78 1.35 1.000 12846.20 8919.53
Days post skilled support intervention 1.11 0.78 1.55 1.000 7409.37 7956.50
Between-Person Effects
Mean perceived persuasion target -> target 1.07 0.63 1.85 1.001 5272.04 7819.99
Mean Perceived persuasion target -> agent 1.12 0.65 1.94 1.001 5349.01 7371.32
Mean Perceived pressure target -> target 1.50 0.76 3.00 1.000 6895.54 8349.79
Mean Perceived pressure target -> agent 0.68 0.31 1.46 1.000 5516.07 7979.83
Mean Perceived pushing target -> target 0.47 0.19 1.18 1.001 5933.99 7858.00
Mean Perceived pushing target -> agent 0.63 0.28 1.43 1.001 7587.94 8442.95
Mean weartime NA NA NA NA NA NA
Difference study group 2 0.80 0.55 1.18 1.000 4638.88 6948.83
Difference study group 3 0.71 0.49 1.04 1.000 4903.37 7219.70
Random Effects
sd(Intercept) 0.68 0.50 0.91 1.00 3179.49 5538.69
sd(Daily perceived persuasion target -> target) 0.21 0.06 0.37 1.00 2371.80 1602.37
sd(Daily perceived persuasion target -> agent) 0.18 0.04 0.33 1.00 3707.08 2508.60
sd(Daily perceived pressure target -> target) 0.16 0.01 0.48 1.00 6671.37 5767.59
sd(Daily perceived pressure target -> agent) 0.15 0.01 0.45 1.00 7438.06 5612.28
sd(Daily perceived pushing target -> target) 0.25 0.02 0.53 1.00 2524.09 2545.36
sd(Daily perceived pushing target -> agent) 0.15 0.01 0.37 1.00 4813.06 4298.98
Additional Parameters
ar[1] 0.02 -0.94 0.94 1.00 13102.70 8324.90
nu NA NA NA NA NA NA
shape 0.14 0.13 0.14 1.00 14533.48 8192.86
sderr 0.05 0.00 0.13 1.00 6664.51 5127.68
sigma NA NA NA NA NA NA

Device Based MVPA

range(df_double$pa_obj, na.rm = T) 
## [1]   5.75 971.25
hist(df_double$pa_obj, breaks = 50)

df_double$pa_obj_log <- log(df_double$pa_obj)

hist(df_double$pa_obj_log, breaks = 50)

We tried negative binomial here as well for consistency, but the model did not converge. Poisson also did not work. As we have no zeros in this distribution, we log transform.

formula <- bf(
  pa_obj_log ~ 
    persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw +
    #barriers_self_cw +
    
    support_self_cw + support_partner_cw + 
    #comf_self_cw + reas_self_cw + 
    isWeekend + 
    got_JITAI_self + skilled_support +
    
    persuasion_self_cb + persuasion_partner_cb +
    pressure_self_cb + pressure_partner_cb +
    pushing_self_cb + pushing_partner_cb +
    
    
    studyGroup +
    
    day + weartime_self_cw + weartime_self_cb +
    
    # Random effects
    (persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw | coupleID),

  autocor = ~ ar(time = day, gr = coupleID:userID, p = 1)
)



prior1 <- c(
  brms::set_prior("normal(0, 2.5)", class = "b"),
  brms::set_prior("normal(0, 50)", class = "Intercept", lb = 0),
  
  brms::set_prior("normal(0, 10)", class = "sd", group = "coupleID", lb = 0),
  
  brms::set_prior("cauchy(0, 5)", class = "ar", lb = -1, ub = 1),
  brms::set_prior("cauchy(0, 10)", class = "sigma", lb = 0)
)


#df_minimal <- df_double[, c("userID", all.vars(as.formula(formula)))]

pa_obj_log <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = gaussian(),
  #control = list(adapt_delta = 0.99),
  iter = 5000,
  warmup = 2000,
  chains = 4,
  cores = 4,
  seed = 7777,
  file = file.path("models_cache_brms", "all_covariates_pa_obj_log")
)
# plotting with the first imputed dataset. 
pp_check(pa_obj_log, type='hist')
## Using 10 posterior draws for ppc type 'hist' by default.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

pp_check(pa_obj_log)
## Using 10 posterior draws for ppc type 'dens_overlay' by default.

loo(pa_obj_log)
## 
## Computed from 12000 by 3333 log-likelihood matrix.
## 
##          Estimate    SE
## elpd_loo  -2804.0  55.8
## p_loo       105.6   4.8
## looic      5607.9 111.6
## ------
## MCSE of elpd_loo is 0.1.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.4, 2.3]).
## 
## All Pareto k estimates are good (k < 0.7).
## See help('pareto-k-diagnostic') for details.
plot(pa_obj_log, ask = FALSE)

summarize_brms(
  pa_obj_log, 
  model_rows_fixed = model_rows_fixed,
  model_rows_random = model_rows_random,
  model_rownames_fixed = model_rownames_fixed,
  model_rownames_random = model_rownames_random,
  exponentiate = T) %>%
  print_df(rows_to_pack = rows_to_pack)
exp(Est.) l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 113.71* 100.07 129.35 1.001 4253.31 6585.20
Within-Person Effects
Daily perceived persuasion target -> target 0.99 0.97 1.02 1.000 22937.49 9284.23
Daily perceived persuasion target -> agent 1.02 1.00 1.04 1.000 25015.90 9446.88
Daily perceived pressure target -> target 1.00 0.95 1.05 1.001 26142.51 8763.25
Daily perceived pressure target -> agent 0.96 0.91 1.02 1.000 24684.48 7569.35
Daily perceived pushing target -> target 1.03* 1.00 1.07 1.001 28242.93 9251.76
Daily perceived pushing target -> agent 0.98 0.95 1.02 1.001 23880.11 8564.22
Day 0.88* 0.79 1.00 1.000 18813.42 10030.83
Daily weartime 1.00 1.00 1.00 1.000 12492.20 8193.11
Daily perceived support target -> target 0.99 0.97 1.00 1.001 23716.02 9023.86
Daily perceived support target -> agent 1.00 0.98 1.01 1.000 24310.05 9653.73
Is a weekend 0.99 0.95 1.04 1.000 27142.07 8077.70
JITAI received 0.96 0.91 1.02 1.000 26334.05 9199.70
Days post skilled support intervention 1.10* 1.01 1.21 1.001 18258.56 9637.22
Between-Person Effects
Mean perceived persuasion target -> target 0.91 0.77 1.08 1.000 9195.93 9765.30
Mean Perceived persuasion target -> agent 1.09 0.91 1.30 1.000 8590.77 9517.42
Mean Perceived pressure target -> target 1.09 0.86 1.38 1.001 15066.71 9845.10
Mean Perceived pressure target -> agent 0.78* 0.63 0.97 1.000 12349.20 9473.69
Mean Perceived pushing target -> target 0.99 0.74 1.32 1.000 11324.00 9349.74
Mean Perceived pushing target -> agent 1.22 0.95 1.55 1.000 13259.91 9433.64
Mean weartime 1.00* 1.00 1.00 1.000 12685.83 10719.60
Difference study group 2 0.97 0.87 1.09 1.001 8224.59 9554.40
Difference study group 3 1.12 0.99 1.26 1.000 8605.56 9657.17
Random Effects
sd(Intercept) 0.27 0.20 0.35 1.00 4178.82 6678.95
sd(Daily perceived persuasion target -> target) 0.06 0.03 0.09 1.00 8618.18 8518.55
sd(Daily perceived persuasion target -> agent) 0.05 0.03 0.08 1.00 8655.75 7996.58
sd(Daily perceived pressure target -> target) 0.06 0.00 0.15 1.00 5447.68 6413.91
sd(Daily perceived pressure target -> agent) 0.04 0.00 0.10 1.00 8636.38 7330.29
sd(Daily perceived pushing target -> target) 0.08 0.01 0.15 1.00 3445.20 3683.34
sd(Daily perceived pushing target -> agent) 0.03 0.00 0.09 1.00 5550.38 6986.72
Additional Parameters
ar[1] 0.28 0.24 0.31 1.00 25198.31 8281.84
nu NA NA NA NA NA NA
shape NA NA NA NA NA NA
sderr NA NA NA NA NA NA
sigma 0.55 0.54 0.57 1.00 22864.76 8154.94 
#summary(pa_obj_log)

Affect

range(df_double$aff, na.rm = T) 
## [1] 1 6
hist(df_double$aff, breaks = 15)

formula <- bf(
  aff ~ 
    persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw +
    #barriers_self_cw +
    
    support_self_cw + support_partner_cw + 
    #comf_self_cw + reas_self_cw + 
    isWeekend + 
    got_JITAI_self + skilled_support +
    
    persuasion_self_cb + persuasion_partner_cb +
    pressure_self_cb + pressure_partner_cb +
    pushing_self_cb + pushing_partner_cb +
    
    
    studyGroup +
    
    day +
    
    # Random effects
    (persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw | coupleID),

  autocor = ~ ar(time = day, gr = coupleID:userID, p = 1)
)



prior1 <- c(
  brms::set_prior("normal(0, 5)", class = "b"),
  brms::set_prior("normal(0, 20)", class = "Intercept", lb=0, ub=6), # range of the outcome scale
  
  brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0),

  brms::set_prior("cauchy(0, 5)", class = "ar", lb = -1, ub = 1),
  brms::set_prior("cauchy(0, 10)", class = "sigma", lb = 0)
  
)


df_minimal <- df_double[, c("userID", all.vars(as.formula(formula)))]

mood <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = gaussian(),
  #control = list(adapt_delta = 0.95, max_treedepth = 15),
  iter = 5000,
  warmup = 2000,
  chains = 4,
  cores = 4,
  seed = 7777,
  file = file.path("models_cache_brms", "all_covariates_mood")
)
pp_check(mood, type='hist')
## Using 10 posterior draws for ppc type 'hist' by default.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

pp_check(mood)
## Using 10 posterior draws for ppc type 'dens_overlay' by default.

loo(mood)
## 
## Computed from 12000 by 3732 log-likelihood matrix.
## 
##          Estimate    SE
## elpd_loo  -4816.2  64.0
## p_loo        99.6   4.6
## looic      9632.5 128.0
## ------
## MCSE of elpd_loo is 0.1.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.4, 2.1]).
## 
## All Pareto k estimates are good (k < 0.7).
## See help('pareto-k-diagnostic') for details.
plot(mood, ask = FALSE)

summarize_brms(
  mood, 
  model_rows_fixed = model_rows_fixed,
  model_rows_random = model_rows_random,
  model_rownames_fixed = model_rownames_fixed,
  model_rownames_random = model_rownames_random,
  exponentiate = F) %>%
  print_df(rows_to_pack = rows_to_pack)
b l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 5.05* 4.78 5.31 1.001 1787.92 4236.13
Within-Person Effects
Daily perceived persuasion target -> target 0.02 -0.01 0.05 1.000 15244.35 9265.67
Daily perceived persuasion target -> agent -0.02 -0.05 0.01 1.000 15536.54 9648.67
Daily perceived pressure target -> target -0.02 -0.09 0.06 1.000 18693.97 8846.55
Daily perceived pressure target -> agent 0.04 -0.04 0.11 1.001 17922.44 8101.11
Daily perceived pushing target -> target -0.03 -0.08 0.01 1.001 17850.56 8603.18
Daily perceived pushing target -> agent -0.02 -0.06 0.03 1.000 18206.81 9527.37
Day -0.06 -0.27 0.14 1.000 10198.11 9336.07
Daily weartime NA NA NA NA NA NA
Daily perceived support target -> target -0.01 -0.04 0.01 1.000 15589.55 9362.03
Daily perceived support target -> agent 0.00 -0.02 0.03 1.000 15979.40 9150.54
Is a weekend 0.06 0.00 0.13 1.000 18191.56 8468.56
JITAI received 0.03 -0.05 0.10 1.001 17601.05 9126.63
Days post skilled support intervention -0.09 -0.24 0.07 1.000 10107.63 9960.36
Between-Person Effects
Mean perceived persuasion target -> target 0.24 -0.08 0.56 1.000 5123.35 7587.45
Mean Perceived persuasion target -> agent 0.05 -0.26 0.36 1.000 5000.31 7650.46
Mean Perceived pressure target -> target 0.13 -0.24 0.49 1.000 9289.56 9066.74
Mean Perceived pressure target -> agent -0.18 -0.56 0.20 1.000 6938.15 8407.71
Mean Perceived pushing target -> target -0.40 -0.93 0.13 1.000 5974.49 8212.38
Mean Perceived pushing target -> agent -0.07 -0.52 0.39 1.000 6773.04 7868.65
Mean weartime NA NA NA NA NA NA
Difference study group 2 -0.25* -0.47 -0.02 1.001 5251.93 7599.96
Difference study group 3 -0.01 -0.22 0.20 1.000 5763.20 7920.22
Random Effects
sd(Intercept) 0.63 0.49 0.81 1.00 2475.39 4700.39
sd(Daily perceived persuasion target -> target) 0.03 0.00 0.07 1.00 5102.77 5489.09
sd(Daily perceived persuasion target -> agent) 0.05 0.01 0.11 1.00 2646.12 3296.59
sd(Daily perceived pressure target -> target) 0.09 0.00 0.26 1.00 3476.20 4494.78
sd(Daily perceived pressure target -> agent) 0.14 0.01 0.32 1.00 3263.89 3747.58
sd(Daily perceived pushing target -> target) 0.09 0.02 0.16 1.00 4357.66 3246.71
sd(Daily perceived pushing target -> agent) 0.08 0.01 0.17 1.00 4128.00 3958.70
Additional Parameters
ar[1] 0.45 0.42 0.48 1.00 15283.87 8183.53
nu NA NA NA NA NA NA
shape NA NA NA NA NA NA
sderr NA NA NA NA NA NA
sigma 0.87 0.85 0.89 1.00 16411.51 8931.96

reactance

range(df_double$reactance, na.rm = T) 
## [1] 0 5
hist(df_double$reactance, breaks = 6)

formula <- bf(
  reactance ~ 
    persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw +
    #barriers_self_cw +
    
    support_self_cw + support_partner_cw + 
    #comf_self_cw + reas_self_cw + 
    isWeekend + 
    got_JITAI_self + skilled_support +
    
    persuasion_self_cb + persuasion_partner_cb +
    pressure_self_cb + pressure_partner_cb +
    pushing_self_cb + pushing_partner_cb +
    
    
    studyGroup +
    
    day +
    
    # Random effects
    (persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw | coupleID),

  autocor = ~ ar(time = day, gr = coupleID:userID, p = 1)
)



prior1 <- c(
  brms::set_prior("normal(0, 5)", class = "b"),
  brms::set_prior("normal(0, 20)", class = "Intercept", lb=0, ub=5), # range of the outcome scale
  
  brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0),

  brms::set_prior("cauchy(0, 5)", class = "ar", lb = -1, ub = 1),
  brms::set_prior("cauchy(0, 10)", class = "sigma", lb = 0)
)


df_minimal <- df_double[, c("userID", all.vars(as.formula(formula)))]

reactance <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = gaussian(),
  #control = list(adapt_delta = 0.95, max_treedepth = 15),
  iter = 5000,
  warmup = 2000,
  chains = 4,
  cores = 4,
  seed = 7777,
  file = file.path("models_cache_brms", "all_covariates_reactance")

)
pp_check(reactance, type='hist')
## Using 10 posterior draws for ppc type 'hist' by default.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

pp_check(reactance)
## Using 10 posterior draws for ppc type 'dens_overlay' by default.

loo(reactance)
## 
## Computed from 12000 by 755 log-likelihood matrix.
## 
##          Estimate   SE
## elpd_loo  -1078.3 33.6
## p_loo        91.4  8.3
## looic      2156.5 67.1
## ------
## MCSE of elpd_loo is NA.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.5, 1.9]).
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. ESS
## (-Inf, 0.7]   (good)     746   98.8%   413     
##    (0.7, 1]   (bad)        9    1.2%   <NA>    
##    (1, Inf)   (very bad)   0    0.0%   <NA>    
## See help('pareto-k-diagnostic') for details.
plot(reactance, ask = FALSE)

summary(reactance)
##  Family: gaussian 
##   Links: mu = identity; sigma = identity 
## Formula: reactance ~ persuasion_self_cw + persuasion_partner_cw + pressure_self_cw + pressure_partner_cw + pushing_self_cw + pushing_partner_cw + support_self_cw + support_partner_cw + isWeekend + got_JITAI_self + skilled_support + persuasion_self_cb + persuasion_partner_cb + pressure_self_cb + pressure_partner_cb + pushing_self_cb + pushing_partner_cb + studyGroup + day + (persuasion_self_cw + persuasion_partner_cw + pressure_self_cw + pressure_partner_cw + pushing_self_cw + pushing_partner_cw | coupleID) 
##          autocor ~ ar(time = day, gr = coupleID:userID, p = 1)
##    Data: data (Number of observations: 755) 
##   Draws: 4 chains, each with iter = 5000; warmup = 2000; thin = 1;
##          total post-warmup draws = 12000
## 
## Correlation Structures:
##       Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## ar[1]     0.01      0.04    -0.07     0.10 1.00    16289    10028
## 
## Multilevel Hyperparameters:
## ~coupleID (Number of levels: 38) 
##                                                Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)                                      0.58      0.09     0.43     0.77 1.00     6019     8283
## sd(persuasion_self_cw)                             0.06      0.04     0.00     0.15 1.00     3677     6662
## sd(persuasion_partner_cw)                          0.04      0.03     0.00     0.11 1.00     8313     7809
## sd(pressure_self_cw)                               0.44      0.10     0.28     0.67 1.00     9028     9596
## sd(pressure_partner_cw)                            0.23      0.15     0.02     0.59 1.00     3616     5689
## sd(pushing_self_cw)                                0.08      0.06     0.00     0.22 1.00     3344     6851
## sd(pushing_partner_cw)                             0.04      0.04     0.00     0.14 1.00     9185     7906
## cor(Intercept,persuasion_self_cw)                 -0.20      0.31    -0.73     0.48 1.00    12183     8672
## cor(Intercept,persuasion_partner_cw)               0.05      0.33    -0.59     0.67 1.00    24220     9239
## cor(persuasion_self_cw,persuasion_partner_cw)     -0.01      0.36    -0.68     0.67 1.00    16798     9659
## cor(Intercept,pressure_self_cw)                    0.30      0.20    -0.13     0.67 1.00     8304     8637
## cor(persuasion_self_cw,pressure_self_cw)          -0.18      0.33    -0.74     0.50 1.00     4111     7291
## cor(persuasion_partner_cw,pressure_self_cw)        0.00      0.35    -0.66     0.66 1.00     4220     7952
## cor(Intercept,pressure_partner_cw)                 0.21      0.25    -0.29     0.69 1.00    12330     9093
## cor(persuasion_self_cw,pressure_partner_cw)        0.01      0.35    -0.65     0.66 1.00    10377     9097
## cor(persuasion_partner_cw,pressure_partner_cw)    -0.02      0.35    -0.67     0.66 1.00     9709     9516
## cor(pressure_self_cw,pressure_partner_cw)          0.03      0.31    -0.56     0.63 1.00    14224     9004
## cor(Intercept,pushing_self_cw)                    -0.01      0.27    -0.54     0.53 1.00    14982     8924
## cor(persuasion_self_cw,pushing_self_cw)           -0.02      0.35    -0.68     0.66 1.00    10657     9588
## cor(persuasion_partner_cw,pushing_self_cw)         0.03      0.36    -0.66     0.68 1.00     9290     9615
## cor(pressure_self_cw,pushing_self_cw)              0.06      0.33    -0.60     0.66 1.00    13253    10342
## cor(pressure_partner_cw,pushing_self_cw)           0.02      0.36    -0.66     0.69 1.00     8850     9565
## cor(Intercept,pushing_partner_cw)                  0.03      0.31    -0.58     0.62 1.00    19738     9074
## cor(persuasion_self_cw,pushing_partner_cw)         0.03      0.35    -0.65     0.70 1.00    18685     8661
## cor(persuasion_partner_cw,pushing_partner_cw)     -0.03      0.36    -0.69     0.65 1.00    13713     9210
## cor(pressure_self_cw,pushing_partner_cw)          -0.06      0.35    -0.70     0.62 1.00    17304    10095
## cor(pressure_partner_cw,pushing_partner_cw)        0.02      0.36    -0.66     0.69 1.00    11850     9697
## cor(pushing_self_cw,pushing_partner_cw)            0.02      0.36    -0.65     0.68 1.00    10836    10468
## 
## Regression Coefficients:
##                                      Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept                                0.58      0.16     0.26     0.90 1.00     7744     8783
## persuasion_self_cw                      -0.01      0.03    -0.06     0.05 1.00    20425     9615
## persuasion_partner_cw                    0.00      0.03    -0.07     0.07 1.00    21357     9939
## pressure_self_cw                        -0.03      0.05    -0.12     0.07 1.00    24948     9144
## pressure_partner_cw                      0.08      0.06    -0.05     0.20 1.00    24546     9646
## pushing_self_cw                         -0.03      0.03    -0.09     0.03 1.00    25660     9310
## pushing_partner_cw                       0.01      0.04    -0.07     0.08 1.00    26664    10055
## support_self_cw                          0.03      0.03    -0.02     0.09 1.00    21817     9584
## support_partner_cw                      -0.01      0.03    -0.06     0.04 1.00    20855    10051
## isWeekendWeekend                        -0.15      0.08    -0.31     0.01 1.00    25252     9397
## got_JITAI_selfJITAIreceived              0.06      0.11    -0.15     0.27 1.00    23904    10276
## skilled_supportDaysafterIntervention    -0.01      0.14    -0.28     0.26 1.00    14510     9639
## persuasion_self_cb                      -0.16      0.20    -0.54     0.23 1.00    10767     9327
## persuasion_partner_cb                    0.12      0.20    -0.27     0.52 1.00    12748     9974
## pressure_self_cb                         0.23      0.22    -0.20     0.67 1.00    13834     9760
## pressure_partner_cb                     -0.11      0.24    -0.58     0.37 1.00    12394     9199
## pushing_self_cb                         -0.16      0.31    -0.78     0.44 1.00    11670     9545
## pushing_partner_cb                       0.03      0.31    -0.57     0.63 1.00    13795     9840
## studyGroupFirst3weeksinterventions      -0.02      0.13    -0.27     0.23 1.00    16460     8698
## studyGrouplast3weeksinterventions        0.18      0.15    -0.11     0.46 1.00    12962     9079
## day                                     -0.16      0.20    -0.56     0.22 1.00    15589     9433
## 
## Further Distributional Parameters:
##       Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma     0.94      0.03     0.89     1.00 1.00    11717     9009
## 
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
summarize_brms(
  reactance, 
  model_rows_fixed = model_rows_fixed,
  model_rows_random = model_rows_random,
  model_rownames_fixed = model_rownames_fixed,
  model_rownames_random = model_rownames_random,
  exponentiate = F) %>%
  print_df(rows_to_pack = rows_to_pack)
b l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 0.58* 0.26 0.90 1.000 7744.35 8782.74
Within-Person Effects
Daily perceived persuasion target -> target -0.01 -0.06 0.05 1.001 20424.84 9614.63
Daily perceived persuasion target -> agent 0.00 -0.07 0.07 1.000 21357.22 9939.32
Daily perceived pressure target -> target -0.03 -0.12 0.07 1.000 24947.66 9143.64
Daily perceived pressure target -> agent 0.08 -0.05 0.20 1.000 24545.74 9645.55
Daily perceived pushing target -> target -0.03 -0.09 0.03 1.000 25660.20 9309.69
Daily perceived pushing target -> agent 0.01 -0.07 0.08 1.000 26663.88 10055.16
Day -0.16 -0.56 0.22 1.000 15588.50 9432.62
Daily weartime NA NA NA NA NA NA
Daily perceived support target -> target 0.03 -0.02 0.09 1.001 21816.79 9583.63
Daily perceived support target -> agent -0.01 -0.06 0.04 1.000 20854.54 10051.05
Is a weekend -0.15 -0.31 0.01 1.000 25252.35 9397.32
JITAI received 0.06 -0.15 0.27 1.000 23904.19 10275.81
Days post skilled support intervention -0.01 -0.28 0.26 1.000 14510.00 9638.81
Between-Person Effects
Mean perceived persuasion target -> target -0.16 -0.54 0.23 1.000 10767.38 9327.50
Mean Perceived persuasion target -> agent 0.12 -0.27 0.52 1.000 12748.11 9974.01
Mean Perceived pressure target -> target 0.23 -0.20 0.67 1.000 13834.11 9760.14
Mean Perceived pressure target -> agent -0.11 -0.58 0.37 1.001 12393.77 9199.46
Mean Perceived pushing target -> target -0.16 -0.78 0.44 1.001 11669.89 9545.08
Mean Perceived pushing target -> agent 0.03 -0.57 0.63 1.000 13795.32 9840.39
Mean weartime NA NA NA NA NA NA
Difference study group 2 -0.02 -0.27 0.23 1.000 16459.57 8698.24
Difference study group 3 0.18 -0.11 0.46 1.000 12962.30 9079.50
Random Effects
sd(Intercept) 0.58 0.43 0.77 1.00 6018.86 8283.19
sd(Daily perceived persuasion target -> target) 0.06 0.00 0.15 1.00 3677.09 6661.98
sd(Daily perceived persuasion target -> agent) 0.04 0.00 0.11 1.00 8313.37 7809.48
sd(Daily perceived pressure target -> target) 0.44 0.28 0.67 1.00 9027.83 9595.62
sd(Daily perceived pressure target -> agent) 0.23 0.02 0.59 1.00 3615.52 5689.37
sd(Daily perceived pushing target -> target) 0.08 0.00 0.22 1.00 3343.60 6851.45
sd(Daily perceived pushing target -> agent) 0.04 0.00 0.14 1.00 9185.20 7905.97
Additional Parameters
ar[1] 0.01 -0.07 0.10 1.00 16289.25 10027.77
nu NA NA NA NA NA NA
shape NA NA NA NA NA NA
sderr NA NA NA NA NA NA
sigma 0.94 0.89 1.00 1.00 11716.99 9009.46

Binary Version

introduce_binary_reactance <- function(data) {
  data$is_reactance <- factor(data$reactance > 0, levels = c(FALSE, TRUE), labels = c(0, 1))
  return(data)
}



df_double <- introduce_binary_reactance(df_double)
if (use_mi) {
  for (i in seq_along(implist)) {
    implist[[i]] <- introduce_binary_reactance(implist[[i]])
  }
}


formula <- bf(
  is_reactance ~ 
    persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw +
    #barriers_self_cw +
    
    support_self_cw + support_partner_cw +
    #comf_self_cw + reas_self_cw + 
    isWeekend + 
    got_JITAI_self + skilled_support +
    
    persuasion_self_cb + persuasion_partner_cb +
    pressure_self_cb + pressure_partner_cb +
    pushing_self_cb + pushing_partner_cb +
    
    
    studyGroup +
    
    day +
    
    # Random effects
    (persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw | coupleID),

  autocor = ~ ar(time = day, gr = coupleID:userID, p = 1)
)



prior1 <- c(
  brms::set_prior("normal(0, 5)", class = "b"),
  brms::set_prior("normal(0, 20)", class = "Intercept", lb=0, ub=5), # range of the outcome scale
  
  brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0),

  brms::set_prior("cauchy(0, 5)", class = "ar", lb = -1, ub = 1)
  #brms::set_prior("cauchy(0, 10)", class = "sigma", lb = 0)
)


df_minimal <- df_double[, c("userID", all.vars(as.formula(formula)))]

is_reactance <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = bernoulli(),
  #control = list(adapt_delta = 0.95, max_treedepth = 15),
  iter = 5000,
  warmup = 2000,
  chains = 4,
  cores = 4,
  seed = 7777,
  file = file.path("models_cache_brms", "all_covariates_is_reactance")

)
pp_check(is_reactance, type='hist')
## Using 10 posterior draws for ppc type 'hist' by default.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

pp_check(is_reactance)
## Using 10 posterior draws for ppc type 'dens_overlay' by default.

try(loo(is_reactance))
## 
## Computed from 12000 by 755 log-likelihood matrix.
## 
##          Estimate   SE
## elpd_loo   -617.7 23.9
## p_loo       570.0 22.7
## looic      1235.5 47.8
## ------
## MCSE of elpd_loo is NA.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.8, 1.2]).
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. ESS
## (-Inf, 0.7]   (good)       2    0.3%   504     
##    (0.7, 1]   (bad)      148   19.6%   <NA>    
##    (1, Inf)   (very bad) 605   80.1%   <NA>    
## See help('pareto-k-diagnostic') for details.
plot(is_reactance, ask = FALSE)

summarize_brms(
  is_reactance, 
  model_rows_fixed = model_rows_fixed,
  model_rows_random = model_rows_random,
  model_rownames_fixed = model_rownames_fixed,
  model_rownames_random = model_rownames_random,
  exponentiate = T) %>%
  print_df(rows_to_pack = rows_to_pack)
OR l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 8.00 0.23 379.95 1.000 8994.85 9710.75
Within-Person Effects
Daily perceived persuasion target -> target 0.79 0.31 1.90 1.000 8128.27 8651.69
Daily perceived persuasion target -> agent 0.89 0.29 2.70 1.000 7901.12 8726.10
Daily perceived pressure target -> target 0.98 0.21 4.87 1.001 9163.07 8832.56
Daily perceived pressure target -> agent 3.13 0.45 27.49 1.001 8551.09 8335.61
Daily perceived pushing target -> target 0.42 0.13 1.15 1.001 8507.10 8661.84
Daily perceived pushing target -> agent 1.10 0.31 3.90 1.000 8729.41 8845.38
Day 0.50 0.00 91.98 1.000 9540.03 8258.20
Daily weartime NA NA NA NA NA NA
Daily perceived support target -> target 1.50 0.65 3.75 1.000 7598.49 8525.59
Daily perceived support target -> agent 0.76 0.29 1.85 1.000 8719.41 8653.74
Is a weekend 0.10 0.01 1.25 1.000 7946.33 8055.38
JITAI received 1.48 0.05 47.00 1.000 8790.24 8814.15
Days post skilled support intervention 0.26 0.00 11.29 1.000 8857.63 9130.37
Between-Person Effects
Mean perceived persuasion target -> target 0.21 0.00 28.89 1.001 9150.33 8719.55
Mean Perceived persuasion target -> agent 7.60 0.08 854.29 1.000 10814.47 9824.59
Mean Perceived pressure target -> target 5.42 0.03 1043.94 1.000 10006.02 9361.64
Mean Perceived pressure target -> agent 0.54 0.00 130.98 1.000 10350.07 9183.34
Mean Perceived pushing target -> target 0.50 0.00 538.20 1.000 11815.93 9995.38
Mean Perceived pushing target -> agent 0.13 0.00 104.50 1.000 11437.32 9098.73
Mean weartime NA NA NA NA NA NA
Difference study group 2 0.70 0.02 31.46 1.000 9026.36 8537.86
Difference study group 3 9.04 0.16 593.72 1.000 9606.96 9289.68
Random Effects
sd(Intercept) 7.48 5.31 10.00 1.00 8837.71 8787.98
sd(Daily perceived persuasion target -> target) 1.75 0.32 3.39 1.00 2637.59 2580.53
sd(Daily perceived persuasion target -> agent) 1.45 0.11 3.24 1.00 2825.34 4515.56
sd(Daily perceived pressure target -> target) 3.74 1.63 6.14 1.00 4573.95 3791.33
sd(Daily perceived pressure target -> agent) 1.28 0.05 3.71 1.00 7738.49 8276.93
sd(Daily perceived pushing target -> target) 0.93 0.03 2.55 1.00 3680.55 7380.74
sd(Daily perceived pushing target -> agent) 0.96 0.04 2.70 1.00 6687.99 7755.31
Additional Parameters
ar[1] 0.14 -0.06 0.34 1.00 2667.32 4936.84
nu NA NA NA NA NA NA
shape NA NA NA NA NA NA
sderr 9.86 6.33 14.34 1.00 4282.36 6561.65
sigma NA NA NA NA NA NA

Report All Models

if (report_ordinal) {
  model_rows_random_final <- model_rows_random_ordinal
  model_rows_fixed_final <- model_rows_fixed_ordinal
  model_rownames_fixed_final <- model_rownames_fixed_ordinal
  model_rownames_random_final <- model_rownames_random_ordinal
  rows_to_pack_final <- rows_to_pack_ordinal
} else {
  model_rows_random_final <- model_rows_random
  model_rows_fixed_final <- model_rows_fixed
  model_rownames_fixed_final <- model_rownames_fixed
  model_rownames_random_final <- model_rownames_random
  rows_to_pack_final <- rows_to_pack
}



all_models <- report_side_by_side(
  pa_sub,
  pa_obj_log,
  mood,
  reactance,
  is_reactance,
  
  model_rows_random = model_rows_random_final,
  model_rows_fixed = model_rows_fixed_final,
  model_rownames_random = model_rownames_random_final,
  model_rownames_fixed = model_rownames_fixed_final
) 
## [1] "pa_sub"
## [1] "pa_obj_log"
## [1] "mood"
## [1] "reactance"
## [1] "is_reactance"
# pretty printing
summary_all_models <- all_models %>%
  print_df(rows_to_pack = rows_to_pack_final) %>%
  add_header_above(
    c(" ", "Subjective MVPA" = 2, 
      "Device-Based MVPA" = 2, 
      "Mood" = 2,
      "Reactance Gaussian" = 2, 
      "Reactance Dichotome" = 2)
  )

export_xlsx(summary_all_models, 
            rows_to_pack = rows_to_pack_final,
            file.path("Output", "SensitivityCovariates", "AllCovariates_SensCovariates.xlsx"),
            merge_option = 'both', 
            simplify_2nd_row = TRUE,
            colwidths = c(40, 7.4, 12.85, 7.4, 12.85,7.4, 12.85,7.4, 12.85,7.4, 12.85),
            line_above_rows = c(1,2,3,28),
            line_below_rows = c(-1))

summary_all_models
Subjective MVPA
Device-Based MVPA
Mood
Reactance Gaussian
Reactance Dichotome
IRR pa_sub 95% CI pa_sub exp(Est.) pa_obj_log 95% CI pa_obj_log b mood 95% CI mood b reactance 95% CI reactance OR is_reactance 95% CI is_reactance
Intercept 31.94* [20.58, 50.03] 113.71* [100.07, 129.35] 5.05* [ 4.78, 5.31] 0.58* [ 0.26, 0.90] 8.00 [0.23, 379.95]
Within-Person Effects
Daily perceived persuasion target -> target 1.03 [ 0.91, 1.16] 0.99 [ 0.97, 1.02] 0.02 [-0.01, 0.05] -0.01 [-0.06, 0.05] 0.79 [0.31, 1.90]
Daily perceived persuasion target -> agent 0.99 [ 0.88, 1.12] 1.02 [ 1.00, 1.04] -0.02 [-0.05, 0.01] 0.00 [-0.07, 0.07] 0.89 [0.29, 2.70]
Daily perceived pressure target -> target 1.22 [ 0.92, 1.66] 1.00 [ 0.95, 1.05] -0.02 [-0.09, 0.06] -0.03 [-0.12, 0.07] 0.98 [0.21, 4.87]
Daily perceived pressure target -> agent 0.93 [ 0.72, 1.25] 0.96 [ 0.91, 1.02] 0.04 [-0.04, 0.11] 0.08 [-0.05, 0.20] 3.13 [0.45, 27.49]
Daily perceived pushing target -> target 1.05 [ 0.89, 1.24] 1.03* [ 1.00, 1.07] -0.03 [-0.08, 0.01] -0.03 [-0.09, 0.03] 0.42 [0.13, 1.15]
Daily perceived pushing target -> agent 0.95 [ 0.80, 1.14] 0.98 [ 0.95, 1.02] -0.02 [-0.06, 0.03] 0.01 [-0.07, 0.08] 1.10 [0.31, 3.90]
Day 0.91 [ 0.58, 1.42] 0.88* [ 0.79, 1.00] -0.06 [-0.27, 0.14] -0.16 [-0.56, 0.22] 0.50 [0.00, 91.98]
Daily weartime NA NA 1.00 [ 1.00, 1.00] NA NA NA NA NA NA
Daily perceived support target -> target 0.91* [ 0.84, 0.99] 0.99 [ 0.97, 1.00] -0.01 [-0.04, 0.01] 0.03 [-0.02, 0.09] 1.50 [0.65, 3.75]
Daily perceived support target -> agent 0.98 [ 0.90, 1.07] 1.00 [ 0.98, 1.01] 0.00 [-0.02, 0.03] -0.01 [-0.06, 0.04] 0.76 [0.29, 1.85]
Is a weekend 1.03 [ 0.84, 1.27] 0.99 [ 0.95, 1.04] 0.06 [ 0.00, 0.13] -0.15 [-0.31, 0.01] 0.10 [0.01, 1.25]
JITAI received 1.02 [ 0.78, 1.35] 0.96 [ 0.91, 1.02] 0.03 [-0.05, 0.10] 0.06 [-0.15, 0.27] 1.48 [0.05, 47.00]
Days post skilled support intervention 1.11 [ 0.78, 1.55] 1.10* [ 1.01, 1.21] -0.09 [-0.24, 0.07] -0.01 [-0.28, 0.26] 0.26 [0.00, 11.29]
Between-Person Effects
Mean perceived persuasion target -> target 1.07 [ 0.63, 1.85] 0.91 [ 0.77, 1.08] 0.24 [-0.08, 0.56] -0.16 [-0.54, 0.23] 0.21 [0.00, 28.89]
Mean Perceived persuasion target -> agent 1.12 [ 0.65, 1.94] 1.09 [ 0.91, 1.30] 0.05 [-0.26, 0.36] 0.12 [-0.27, 0.52] 7.60 [0.08, 854.29]
Mean Perceived pressure target -> target 1.50 [ 0.76, 3.00] 1.09 [ 0.86, 1.38] 0.13 [-0.24, 0.49] 0.23 [-0.20, 0.67] 5.42 [0.03, 1043.94]
Mean Perceived pressure target -> agent 0.68 [ 0.31, 1.46] 0.78* [ 0.63, 0.97] -0.18 [-0.56, 0.20] -0.11 [-0.58, 0.37] 0.54 [0.00, 130.98]
Mean Perceived pushing target -> target 0.47 [ 0.19, 1.18] 0.99 [ 0.74, 1.32] -0.40 [-0.93, 0.13] -0.16 [-0.78, 0.44] 0.50 [0.00, 538.20]
Mean Perceived pushing target -> agent 0.63 [ 0.28, 1.43] 1.22 [ 0.95, 1.55] -0.07 [-0.52, 0.39] 0.03 [-0.57, 0.63] 0.13 [0.00, 104.50]
Mean weartime NA NA 1.00* [ 1.00, 1.00] NA NA NA NA NA NA
Difference study group 2 0.80 [ 0.55, 1.18] 0.97 [ 0.87, 1.09] -0.25* [-0.47, -0.02] -0.02 [-0.27, 0.23] 0.70 [0.02, 31.46]
Difference study group 3 0.71 [ 0.49, 1.04] 1.12 [ 0.99, 1.26] -0.01 [-0.22, 0.20] 0.18 [-0.11, 0.46] 9.04 [0.16, 593.72]
Random Effects
sd(Intercept) 0.68 [ 0.50, 0.91] 0.27 [0.20, 0.35] 0.63 [0.49, 0.81] 0.58 [ 0.43, 0.77] 7.48 [ 5.31, 10.00]
sd(Daily perceived persuasion target -> target) 0.21 [ 0.06, 0.37] 0.06 [0.03, 0.09] 0.03 [0.00, 0.07] 0.06 [ 0.00, 0.15] 1.75 [ 0.32, 3.39]
sd(Daily perceived persuasion target -> agent) 0.18 [ 0.04, 0.33] 0.05 [0.03, 0.08] 0.05 [0.01, 0.11] 0.04 [ 0.00, 0.11] 1.45 [ 0.11, 3.24]
sd(Daily perceived pressure target -> target) 0.16 [ 0.01, 0.48] 0.06 [0.00, 0.15] 0.09 [0.00, 0.26] 0.44 [ 0.28, 0.67] 3.74 [ 1.63, 6.14]
sd(Daily perceived pressure target -> agent) 0.15 [ 0.01, 0.45] 0.04 [0.00, 0.10] 0.14 [0.01, 0.32] 0.23 [ 0.02, 0.59] 1.28 [ 0.05, 3.71]
sd(Daily perceived pushing target -> target) 0.25 [ 0.02, 0.53] 0.08 [0.01, 0.15] 0.09 [0.02, 0.16] 0.08 [ 0.00, 0.22] 0.93 [ 0.03, 2.55]
sd(Daily perceived pushing target -> agent) 0.15 [ 0.01, 0.37] 0.03 [0.00, 0.09] 0.08 [0.01, 0.17] 0.04 [ 0.00, 0.14] 0.96 [ 0.04, 2.70]
Additional Parameters
ar[1] 0.02 [-0.94, 0.94] 0.28 [0.24, 0.31] 0.45 [0.42, 0.48] 0.01 [-0.07, 0.10] 0.14 [-0.06, 0.34]
nu NA NA NA NA NA NA NA NA NA NA
shape 0.14 [ 0.13, 0.14] NA NA NA NA NA NA NA NA
sderr 0.05 [ 0.00, 0.13] NA NA NA NA NA NA 9.86 [ 6.33, 14.34]
sigma NA NA 0.55 [0.54, 0.57] 0.87 [0.85, 0.89] 0.94 [ 0.89, 1.00] NA NA 
report::report_system()

Analyses were conducted using the R Statistical language (version 4.4.1; R Core Team, 2024) on Windows 11 x64 (build 22635)

report::cite_packages()